Both agriculture and the environment at-large heavily depend on pollinating insects to grow properly. However, widespread use of pesticides and a multitude of parasitic infections have led to the honey bee suffering from Colony Collapse Disorder in which their populations greatly dwindle. To overcome this, native pollinators can help support the struggling honey bee while sometimes even being more efficient in the process. A team of three students from the University of Illinois, Jyotsna Joshi, Keerat Singh, and Piyush Sud, have created a smart beehive project that enables beekeepers to better monitor the population size of Mason Bees.
This sensor-equipped beehive is meant to resemble the natural environment in which the mason bee resides, as it contains dark areas connected via tunnels where the bees can burrow. Due to this tunneling aspect, the structure contains several capacitive sensors near the entrances and exits that can non-intrusively detect when a bee is entering or leaving. From here, the data is logged using a microcontroller for later viewing and analysis.
The house for the mason bees is comprised of a simple wooden, framed structure that is capped with a sloped roof where the eggs reside. Inside are three tunnel slots made up of a single acrylic tunnel each, along with space for the electronics. The team took great care to ensure the size and layout were conducive to sustaining a healthy mason bee population, as anything too large or too small would simply be ignored by the bees.
A single acrylic tunnel was selected to have a series of three small conductive rings placed around the outside and connected to an alternating current wave generator. Any bee passing through the tunnel would cause a small disturbance in the field, which could be amplified, demodulated, and passed along to an ATmega328P microcontroller. It collects readings from each of the three analog pins connected to the capacitors at regular intervals and records them to a CSV file on an attached SD card.
After the system has been running for a sufficiently long time, the beekeeper can easily remove the SD card and read the file into a separate Python script the team wrote. In essence, the script takes the time-series values and performs several analyses on them, including raw data, rolling averages, statistical outliers, finding points above two standard deviations, and marking where a bee might have entered or exited the hive before outputting the figure. For more information about this project, you can read the team's write-up here or in their paper.